Niemann-Pick disease type C (NP-C) (OMIM#257220) is a rare lysosomal storage disorder caused by pathogenic variants in either the NPC1 or NPC2 genes. It manifests with a wide spectrum of clinical symptoms and variable age of onset. We studied the impact of the frequent polymorphic variant c.2793?C?&gt;?T (p.Asn931?=?), located in the donor splice site (SS) of NPC1 exon 18 on the penetrance of the rare synonymous variant c.2727?C?&gt;?T (p.Cys909?=?), identified in two 55?y.o. twins with an adult onset form of NP-C. The patients' diagnosis was supported by biochemical analysis and positive filipin test. Analysis of the patients' cDNA showed that the c.2727?C?&gt;?T variant leads to cryptic donor SS activation and frameshift deletion in the NPC1 exon 18. However, the minigene assay demonstrated that this exon shortening takes place only in the presence of the frequent polymorphic variant c.2793?C?&gt;?T. Results of the transcript specific qPCR showed that only the presence in the NPC1 exon 18 of both variants leads to significant decrease of wild type (WT) transcript isoform.Hereditary spastic paraplegias (HSP) are heterogeneous disorders, with more than 70 causative genes. Variants in SPAST are the most frequent genetic etiology and are responsible for spastic paraplegia type 4 (SPG4). Age at onset can vary, even between patients from the same family, and incomplete penetrance is described. Somatic mosaicism is extremely rare with only three patients reported in the literature. We report here SPAST mosaic variants in four unrelated patients. We confirm that mosaicism in SPAST is a very rare event with only four identified cases on more than 300 patients with a SPAST variant previously described by our clinical diagnostic laboratory.Children with sickle cell disease (SCD) are at high-risk of progressive, chronic pulmonary and cardiac dysfunction. In this prospective multicenter Phase II trial of myeloimmunoablative conditioning followed by haploidentical stem cell transplantation in children with high-risk SCD, 19 patients, 2.0-21.0 years of age, were enrolled with one or more of the following history of (1) overt stroke; (2) silent stroke; (3) elevated transcranial Doppler velocity; (4) multiple vaso-occlusive crises; and/or (5) two or more acute chest syndromes and received haploidentical transplants from 18 parental donors. Cardiac and pulmonary centralized cores were established. Pulmonary function results were expressed as percent of the median of healthy reference cohorts, matched for age, sex, height and race. At 2 years, pulmonary functions including forced expiratory volume (FEV), FEV1/ forced vital capacity (FVC), total lung capacity (TLC), diffusing capacity of lung for carbon monoxide (DLCO) were stable to improved compared to baseline values. Importantly, specific airway conductance was significantly improved at 2 years (p? less then ?0.004). Left ventricular systolic function (fractional shortening) and tricuspid regurgitant velocity were stable at 2 years. These results demonstrate that haploidentical stem cell transplantation can stabilize or improve cardiopulmonary function in patients with SCD.The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learning classifiers for age-related macular degeneration (AMD). Artificial intelligence diagnostic algorithms can automatically detect and diagnose AMD through training data from large sets of fundus or OCT images. The use of AI algorithms is a powerful tool, and it is a method of obtaining a cost-effective, simple, and fast diagnosis of AMD.
MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software and data on sensitivity, specificity and area under curve were extracted from the included studies. STATA 15.0 was used to conduct the meta-analysis.
Our search strategy identified 307 records from online databases and 174 records from gray literature. Total of 13 records, 64,798 subjects (and 612,429 images), were used for the quantitative analysis. The pooled estimate for sensitivity was 0.918 [95% CI 0.678, 0.98] and specificity was 0.888 [95% CI 0.578, 0.98] for AMD screening using machine learning classifiers. https://www.selleckchem.com/products/brequinar.html The relative odds of a positive screen test in AMD cases were 89.74 [95% CI 3.05-2641.59] times more likely than a negative screen test in non-AMD cases. The positive likelihood ratio was 8.22 [95% CI 1.52-44.48] and the negative likelihood ratio was 0.09 [95% CI 0.02-0.52].
The included studies show promising results for the diagnostic accuracy of the machine learning classifiers for AMD and its implementation in clinical settings.
The included studies show promising results for the diagnostic accuracy of the machine learning classifiers for AMD and its implementation in clinical settings.Sickle cell retinopathy is often initially asymptomatic even in proliferative stages, but can progress to cause vision loss due to vitreous haemorrhages or tractional retinal detachments. Challenges with access and adherence to screening dilated fundus examinations, particularly in medically underserved areas where the burden of sickle cell disease is highest, highlight the need for novel approaches to screening for patients with vision-threatening sickle cell retinopathy. This article reviews the existing literature on and suggests future research directions for coupling artificial intelligence with multimodal retinal imaging to expand access to automated, accurate, imaging-based screening for sickle cell retinopathy. Given the variability in retinal specialist practice patterns with regards to monitoring and treatment of sickle cell retinopathy, we also discuss recent progress toward development of machine learning models that can quantitatively track disease progression over time. These artificial intelligence-based applications have great potential for informing evidence-based and resource-efficient clinical diagnosis and management of sickle cell retinopathy.